- Badih Ghazi
- Pasin Manurangsi
- Ravi Kumar Ravikumar
- Thomas Alexander Steinke
ITCS 2023 (to appear)
Differential privacy is often applied with a privacy parameter that is larger than the theory suggests is ideal; various informal justifications for tolerating large privacy parameters have been proposed.
In this work, we consider partial differential privacy (DP), which allows quantifying the privacy guarantee on a per-attribute basis.
In this framework, we study several basic data analysis and learning tasks, and design algorithms whose per-attribute privacy parameter is smaller that the best possible privacy parameter for the entire record of a person (i.e., all the attributes).
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